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                <h1 class="post-title" itemprop="name headline"> TensorFlow layers模块用法 </h1>
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                <p>TensorFlow 中的 layers 模块提供用于深度学习的更高层次封装的 API，利用它我们可以轻松地构建模型，这一节我们就来看下这个模块的 API 的具体用法。</p>
                <h2 id="概览"><a href="#概览" class="headerlink" title="概览"></a>概览</h2>
                <p>layers 模块的路径写法为 tf.layers，这个模块定义在 tensorflow/python/layers/layers.py，其官方文档地址为：<a href="https://www.tensorflow.org/api_docs/python/tf/layers" target="_blank" rel="noopener">https://www.tensorflow.org/api_docs/python/tf/layers</a>，TensorFlow 版本为 1.5。 这里面提供了多个类和方法以供使用，下面我们分别予以介绍。</p>
                <h2 id="方法"><a href="#方法" class="headerlink" title="方法"></a>方法</h2>
                <p>tf.layers 模块提供的方法有：</p>
                <ul>
                  <li>Input(…): 用于实例化一个输入 Tensor，作为神经网络的输入。</li>
                  <li>average_pooling1d(…): 一维平均池化层</li>
                  <li>average_pooling2d(…): 二维平均池化层</li>
                  <li>average_pooling3d(…): 三维平均池化层</li>
                  <li>batch_normalization(…): 批量标准化层</li>
                  <li>conv1d(…): 一维卷积层</li>
                  <li>conv2d(…): 二维卷积层</li>
                  <li>conv2d_transpose(…): 二维反卷积层</li>
                  <li>conv3d(…): 三维卷积层</li>
                  <li>conv3d_transpose(…): 三维反卷积层</li>
                  <li>dense(…): 全连接层</li>
                  <li>dropout(…): Dropout层</li>
                  <li>flatten(…): Flatten层，即把一个 Tensor 展平</li>
                  <li>max_pooling1d(…): 一维最大池化层</li>
                  <li>max_pooling2d(…): 二维最大池化层</li>
                  <li>max_pooling3d(…): 三维最大池化层</li>
                  <li>separable_conv2d(…): 二维深度可分离卷积层</li>
                </ul>
                <h3 id="Input"><a href="#Input" class="headerlink" title="Input"></a>Input</h3>
                <p>tf.layers.Input() 这个方法是用于输入数据的方法，其实类似于 tf.placeholder，相当于一个占位符的作用，当然也可以通过传入 tensor 参数来进行赋值。</p>
                <figure class="highlight routeros">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">Input(</span><br><span class="line">    <span class="attribute">shape</span>=None,</span><br><span class="line">    <span class="attribute">batch_size</span>=None,</span><br><span class="line">    <span class="attribute">name</span>=None,</span><br><span class="line">    <span class="attribute">dtype</span>=tf.float32,</span><br><span class="line">    <span class="attribute">sparse</span>=<span class="literal">False</span>,</span><br><span class="line">    <span class="attribute">tensor</span>=None</span><br><span class="line">)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>参数说明如下：</p>
                <ul>
                  <li>shape：可选，默认 None，是一个数字组成的元组或列表，但是这个 shape 比较特殊，它不包含 batch_size，比如传入的 shape 为 [32]，那么它会将 shape 转化为 [?, 32]，这里一定需要注意。</li>
                  <li>batch_size：可选，默认 None，代表输入数据的 batch size，可以是数字或者 None。</li>
                  <li>name：可选，默认 None，输入层的名称。</li>
                  <li>dtype：可选，默认 tf.float32，元素的类型。</li>
                  <li>sparse：可选，默认 False，指定是否以稀疏矩阵的形式来创建 placeholder。</li>
                  <li>tensor：可选，默认 None，如果指定，那么创建的内容便不再是一个 placeholder，会用此 Tensor 初始化。</li>
                </ul>
                <p>返回值： 返回一个包含历史 Meta Data 的 Tensor。 我们用一个实例来感受一下：</p>
                <figure class="highlight stylus">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">x = tf<span class="selector-class">.layers</span>.Input(shape=[<span class="number">32</span>])</span><br><span class="line"><span class="function"><span class="title">print</span><span class="params">(x)</span></span></span><br><span class="line">y = tf<span class="selector-class">.layers</span>.dense(x, <span class="number">16</span>, activation=tf<span class="selector-class">.nn</span>.softmax)</span><br><span class="line"><span class="function"><span class="title">print</span><span class="params">(y)</span></span></span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>首先我们用 Input() 方法初始化了一个 placeholder，这时我们没有传入 tensor 参数，然后调用了 dense() 方法构建了一个全连接网络，激活函数使用 softmax，然后将二者输出，结果如下：</p>
                <figure class="highlight stylus">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line"><span class="function"><span class="title">Tensor</span><span class="params">(<span class="string">"input_layer_1:0"</span>, shape=(?, <span class="number">32</span>)</span></span>, dtype=float32)</span><br><span class="line"><span class="function"><span class="title">Tensor</span><span class="params">(<span class="string">"dense/Softmax:0"</span>, shape=(?, <span class="number">16</span>)</span></span>, dtype=float32)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>这时我们发现，shape 它给我们做了转化，本来是 [32]，结果它给转化成了 [?, 32]，即第一维代表 batch_size，所以我们需要注意，在调用此方法的时候不需要去关心 batch_size 这一维。 如果我们在初始化的时候传入一个已有 Tensor，例如：</p>
                <figure class="highlight haskell">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line"><span class="class"><span class="keyword">data</span> = tf.constant([1, 2, 3])</span></span><br><span class="line"><span class="title">x</span> = tf.layers.<span class="type">Input</span>(tensor=<span class="class"><span class="keyword">data</span>)</span></span><br><span class="line"><span class="title">print</span>(x)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>结果如下：</p>
                <figure class="highlight reasonml">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line"><span class="constructor">Tensor(<span class="string">"Const:0"</span>, <span class="params">shape</span>=(3,)</span>, dtype=<span class="built_in">int32</span>)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>可以看到它可以自动计算出其 shape 和 dtype。</p>
                <h3 id="batch-normalization"><a href="#batch-normalization" class="headerlink" title="batch_normalization"></a>batch_normalization</h3>
                <p>此方法是批量标准化的方法，经过处理之后可以加速训练速度，其定义在 tensorflow/python/layers/normalization.py，论文可以参考：<a href="http://arxiv.org/abs/1502.03167" target="_blank" rel="noopener">http://arxiv.org/abs/1502.03167</a> “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift”。</p>
                <figure class="highlight routeros">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">batch_normalization(</span><br><span class="line">    inputs,</span><br><span class="line">    <span class="attribute">axis</span>=-1,</span><br><span class="line">    <span class="attribute">momentum</span>=0.99,</span><br><span class="line">    <span class="attribute">epsilon</span>=0.001,</span><br><span class="line">    <span class="attribute">center</span>=<span class="literal">True</span>,</span><br><span class="line">    <span class="attribute">scale</span>=<span class="literal">True</span>,</span><br><span class="line">    <span class="attribute">beta_initializer</span>=tf.zeros_initializer(),</span><br><span class="line">    <span class="attribute">gamma_initializer</span>=tf.ones_initializer(),</span><br><span class="line">    <span class="attribute">moving_mean_initializer</span>=tf.zeros_initializer(),</span><br><span class="line">    <span class="attribute">moving_variance_initializer</span>=tf.ones_initializer(),</span><br><span class="line">    <span class="attribute">beta_regularizer</span>=None,</span><br><span class="line">    <span class="attribute">gamma_regularizer</span>=None,</span><br><span class="line">    <span class="attribute">beta_constraint</span>=None,</span><br><span class="line">    <span class="attribute">gamma_constraint</span>=None,</span><br><span class="line">    <span class="attribute">training</span>=<span class="literal">False</span>,</span><br><span class="line">    <span class="attribute">trainable</span>=<span class="literal">True</span>,</span><br><span class="line">    <span class="attribute">name</span>=None,</span><br><span class="line">    <span class="attribute">reuse</span>=None,</span><br><span class="line">    <span class="attribute">renorm</span>=<span class="literal">False</span>,</span><br><span class="line">    <span class="attribute">renorm_clipping</span>=None,</span><br><span class="line">    <span class="attribute">renorm_momentum</span>=0.99,</span><br><span class="line">    <span class="attribute">fused</span>=None,</span><br><span class="line">    <span class="attribute">virtual_batch_size</span>=None,</span><br><span class="line">    <span class="attribute">adjustment</span>=None</span><br><span class="line">)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>参数说明如下：</p>
                <ul>
                  <li>inputs：必需，即输入数据。</li>
                  <li>axis：可选，默认 -1，即进行标注化操作时操作数据的哪个维度。</li>
                  <li>momentum：可选，默认 0.99，即动态均值的动量。</li>
                  <li>epsilon：可选，默认 0.01，大于0的小浮点数，用于防止除0错误。</li>
                  <li>center：可选，默认 True，若设为True，将会将 beta 作为偏置加上去，否则忽略参数 beta</li>
                  <li>scale：可选，默认 True，若设为True，则会乘以gamma，否则不使用gamma。当下一层是线性的时，可以设False，因为scaling的操作将被下一层执行。</li>
                  <li>beta_initializer：可选，默认 zeros_initializer，即 beta 权重的初始方法。</li>
                  <li>gamma_initializer：可选，默认 ones_initializer，即 gamma 的初始化方法。</li>
                  <li>moving_mean_initializer：可选，默认 zeros_initializer，即动态均值的初始化方法。</li>
                  <li>moving_variance_initializer：可选，默认 ones_initializer，即动态方差的初始化方法。</li>
                  <li>beta_regularizer: 可选，默认None，beta 的正则化方法。</li>
                  <li>gamma_regularizer: 可选，默认None，gamma 的正则化方法。</li>
                  <li>beta_constraint: 可选，默认None，加在 beta 上的约束项。</li>
                  <li>gamma_constraint: 可选，默认None，加在 gamma 上的约束项。</li>
                  <li>training：可选，默认 False，返回结果是 training 模式。</li>
                  <li>trainable：可选，默认为 True，布尔类型，如果为 True，则将变量添加 GraphKeys.TRAINABLE_VARIABLES 中。</li>
                  <li>name：可选，默认 None，层名称。</li>
                  <li>reuse：可选，默认 None，根据层名判断是否重复利用。</li>
                  <li>renorm：可选，默认 False，是否要用 Batch Renormalization (<a href="https://arxiv.org/abs/1702.03275" target="_blank" rel="noopener">https://arxiv.org/abs/1702.03275</a>)</li>
                  <li>renorm_clipping：可选，默认 None，是否要用 rmax、rmin、dmax 来 scalar Tensor。</li>
                  <li>renorm_momentum，可选，默认 0.99，用来更新动态均值和标准差的 Momentum 值。</li>
                  <li>fused，可选，默认 None，是否使用一个更快的、融合的实现方法。</li>
                  <li>virtual_batch_size，可选，默认 None，是一个 int 数字，指定一个虚拟 batch size。</li>
                  <li>adjustment，可选，默认 None，对标准化后的结果进行适当调整的方法。</li>
                </ul>
                <p>最后的一些参数说明不够详尽，更详细的用法参考：<a href="https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization" target="_blank" rel="noopener">https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization</a>。 其用法很简单，在输入数据后面加一层 batch_normalization() 即可：</p>
                <figure class="highlight ini">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line"><span class="attr">x</span> = tf.layers.Input(shape=[<span class="number">32</span>])</span><br><span class="line"><span class="attr">x</span> = tf.layers.batch_normalization(x)</span><br><span class="line"><span class="attr">y</span> = tf.layers.dense(x, <span class="number">20</span>)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <h3 id="dense"><a href="#dense" class="headerlink" title="dense"></a>dense</h3>
                <p>dense，即全连接网络，layers 模块提供了一个 dense() 方法来实现此操作，定义在 tensorflow/python/layers/core.py 中，下面我们来说明一下它的用法。</p>
                <figure class="highlight routeros">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">dense(</span><br><span class="line">    inputs,</span><br><span class="line">    units,</span><br><span class="line">    <span class="attribute">activation</span>=None,</span><br><span class="line">    <span class="attribute">use_bias</span>=<span class="literal">True</span>,</span><br><span class="line">    <span class="attribute">kernel_initializer</span>=None,</span><br><span class="line">    <span class="attribute">bias_initializer</span>=tf.zeros_initializer(),</span><br><span class="line">    <span class="attribute">kernel_regularizer</span>=None,</span><br><span class="line">    <span class="attribute">bias_regularizer</span>=None,</span><br><span class="line">    <span class="attribute">activity_regularizer</span>=None,</span><br><span class="line">    <span class="attribute">kernel_constraint</span>=None,</span><br><span class="line">    <span class="attribute">bias_constraint</span>=None,</span><br><span class="line">    <span class="attribute">trainable</span>=<span class="literal">True</span>,</span><br><span class="line">    <span class="attribute">name</span>=None,</span><br><span class="line">    <span class="attribute">reuse</span>=None</span><br><span class="line">)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>参数说明如下：</p>
                <ul>
                  <li>inputs：必需，即需要进行操作的输入数据。</li>
                  <li>units：必须，即神经元的数量。</li>
                  <li>activation：可选，默认为 None，如果为 None 则是线性激活。</li>
                  <li>use_bias：可选，默认为 True，是否使用偏置。</li>
                  <li>kernel_initializer：可选，默认为 None，即权重的初始化方法，如果为 None，则使用默认的 Xavier 初始化方法。</li>
                  <li>bias_initializer：可选，默认为零值初始化，即偏置的初始化方法。</li>
                  <li>kernel_regularizer：可选，默认为 None，施加在权重上的正则项。</li>
                  <li>bias_regularizer：可选，默认为 None，施加在偏置上的正则项。</li>
                  <li>activity_regularizer：可选，默认为 None，施加在输出上的正则项。</li>
                  <li>kernel_constraint，可选，默认为 None，施加在权重上的约束项。</li>
                  <li>bias_constraint，可选，默认为 None，施加在偏置上的约束项。</li>
                  <li>trainable：可选，默认为 True，布尔类型，如果为 True，则将变量添加到 GraphKeys.TRAINABLE_VARIABLES 中。</li>
                  <li>name：可选，默认为 None，卷积层的名称。</li>
                  <li>reuse：可选，默认为 None，布尔类型，如果为 True，那么如果 name 相同时，会重复利用。</li>
                </ul>
                <p>返回值： 全连接网络处理后的 Tensor。 下面我们用一个实例来感受一下它的用法：</p>
                <figure class="highlight stylus">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">x = tf<span class="selector-class">.layers</span>.Input(shape=[<span class="number">32</span>])</span><br><span class="line"><span class="function"><span class="title">print</span><span class="params">(x)</span></span></span><br><span class="line">y1 = tf<span class="selector-class">.layers</span>.dense(x, <span class="number">16</span>, activation=tf<span class="selector-class">.nn</span>.relu)</span><br><span class="line"><span class="function"><span class="title">print</span><span class="params">(y1)</span></span></span><br><span class="line">y2 = tf<span class="selector-class">.layers</span>.dense(y1, <span class="number">5</span>, activation=tf<span class="selector-class">.nn</span>.sigmoid)</span><br><span class="line"><span class="function"><span class="title">print</span><span class="params">(y2)</span></span></span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>首先我们用 Input 定义了 [?, 32] 的输入数据，然后经过第一层全连接网络，此时指定了神经元个数为 16，激活函数为 relu，接着输出结果经过第二层全连接网络，此时指定了神经元个数为 5，激活函数为 sigmoid，最后输出，结果如下：</p>
                <figure class="highlight stylus">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line"><span class="function"><span class="title">Tensor</span><span class="params">(<span class="string">"input_layer_1:0"</span>, shape=(?, <span class="number">32</span>)</span></span>, dtype=float32)</span><br><span class="line"><span class="function"><span class="title">Tensor</span><span class="params">(<span class="string">"dense/Relu:0"</span>, shape=(?, <span class="number">16</span>)</span></span>, dtype=float32)</span><br><span class="line"><span class="function"><span class="title">Tensor</span><span class="params">(<span class="string">"dense_2/Sigmoid:0"</span>, shape=(?, <span class="number">5</span>)</span></span>, dtype=float32)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>可以看到输出结果的最后一维度就等于神经元的个数，这是非常容易理解的。</p>
                <h3 id="convolution"><a href="#convolution" class="headerlink" title="convolution"></a>convolution</h3>
                <p>convolution，即卷积，这里提供了多个卷积方法，如 conv1d()、conv2d()、conv3d()，分别代表一维、二维、三维卷积，另外还有 conv2d_transpose()、conv3d_transpose()，分别代表二维和三维反卷积，还有 separable_conv2d() 方法代表二维深度可分离卷积。它们定义在 tensorflow/python/layers/convolutional.py 中，其用法都是类似的，在这里以 conv2d() 方法为例进行说明。</p>
                <figure class="highlight routeros">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">conv2d(</span><br><span class="line">    inputs,</span><br><span class="line">    filters,</span><br><span class="line">    kernel_size,</span><br><span class="line">    strides=(1, 1),</span><br><span class="line">    <span class="attribute">padding</span>=<span class="string">'valid'</span>,</span><br><span class="line">    <span class="attribute">data_format</span>=<span class="string">'channels_last'</span>,</span><br><span class="line">    dilation_rate=(1, 1),</span><br><span class="line">    <span class="attribute">activation</span>=None,</span><br><span class="line">    <span class="attribute">use_bias</span>=<span class="literal">True</span>,</span><br><span class="line">    <span class="attribute">kernel_initializer</span>=None,</span><br><span class="line">    <span class="attribute">bias_initializer</span>=tf.zeros_initializer(),</span><br><span class="line">    <span class="attribute">kernel_regularizer</span>=None,</span><br><span class="line">    <span class="attribute">bias_regularizer</span>=None,</span><br><span class="line">    <span class="attribute">activity_regularizer</span>=None,</span><br><span class="line">    <span class="attribute">kernel_constraint</span>=None,</span><br><span class="line">    <span class="attribute">bias_constraint</span>=None,</span><br><span class="line">    <span class="attribute">trainable</span>=<span class="literal">True</span>,</span><br><span class="line">    <span class="attribute">name</span>=None,</span><br><span class="line">    <span class="attribute">reuse</span>=None</span><br><span class="line">)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>参数说明如下：</p>
                <ul>
                  <li>inputs：必需，即需要进行操作的输入数据。</li>
                  <li>filters：必需，是一个数字，代表了输出通道的个数，即 output_channels。</li>
                  <li>kernel_size：必需，卷积核大小，必须是一个数字（高和宽都是此数字）或者长度为 2 的列表（分别代表高、宽）。</li>
                  <li>strides：可选，默认为 (1, 1)，卷积步长，必须是一个数字（高和宽都是此数字）或者长度为 2 的列表（分别代表高、宽）。</li>
                  <li>padding：可选，默认为 valid，padding 的模式，有 valid 和 same 两种，大小写不区分。</li>
                  <li>data_format：可选，默认 channels_last，分为 channels_last 和 channels_first 两种模式，代表了输入数据的维度类型，如果是 channels_last，那么输入数据的 shape 为 (batch, height, width, channels)，如果是 channels_first，那么输入数据的 shape 为 (batch, channels, height, width)。</li>
                  <li>dilation_rate：可选，默认为 (1, 1)，卷积的扩张率，如当扩张率为 2 时，卷积核内部就会有边距，3x3 的卷积核就会变成 5x5。</li>
                  <li>activation：可选，默认为 None，如果为 None 则是线性激活。</li>
                  <li>use_bias：可选，默认为 True，是否使用偏置。</li>
                  <li>kernel_initializer：可选，默认为 None，即权重的初始化方法，如果为 None，则使用默认的 Xavier 初始化方法。</li>
                  <li>bias_initializer：可选，默认为零值初始化，即偏置的初始化方法。</li>
                  <li>kernel_regularizer：可选，默认为 None，施加在权重上的正则项。</li>
                  <li>bias_regularizer：可选，默认为 None，施加在偏置上的正则项。</li>
                  <li>activity_regularizer：可选，默认为 None，施加在输出上的正则项。</li>
                  <li>kernel_constraint，可选，默认为 None，施加在权重上的约束项。</li>
                  <li>bias_constraint，可选，默认为 None，施加在偏置上的约束项。</li>
                  <li>trainable：可选，默认为 True，布尔类型，如果为 True，则将变量添加到 GraphKeys.TRAINABLE_VARIABLES 中。</li>
                  <li>name：可选，默认为 None，卷积层的名称。</li>
                  <li>reuse：可选，默认为 None，布尔类型，如果为 True，那么如果 name 相同时，会重复利用。</li>
                </ul>
                <p>返回值： 卷积后的 Tensor。 下面我们用实例感受一下它的用法：</p>
                <figure class="highlight routeros">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">x = tf.layers.Input(shape=[20, 20, 3])</span><br><span class="line">y = tf.layers.conv2d(x, <span class="attribute">filters</span>=6, <span class="attribute">kernel_size</span>=2, <span class="attribute">padding</span>=<span class="string">'same'</span>)</span><br><span class="line"><span class="builtin-name">print</span>(y)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>这里我们首先声明了一个 [?, 20, 20, 3] 的输入 x，然后将其传给 conv2d() 方法，filters 设定为 6，即输出通道为 6，kernel_size 为 2，即卷积核大小为 2 x 2，padding 方式设置为 same，那么输出结果的宽高和原来一定是相同的，但是输出通道就变成了 6，结果如下：</p>
                <figure class="highlight angelscript">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">Tensor(<span class="string">"conv2d/BiasAdd:0"</span>, shape=(?, <span class="number">20</span>, <span class="number">20</span>, <span class="number">6</span>), dtype=<span class="built_in">float</span>32)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>但如果我们将 padding 方式不传入，使用默认的 valid 模式，代码改写如下：</p>
                <figure class="highlight routeros">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">x = tf.layers.Input(shape=[20, 20, 3])</span><br><span class="line">y = tf.layers.conv2d(x, <span class="attribute">filters</span>=6, <span class="attribute">kernel_size</span>=2)</span><br><span class="line"><span class="builtin-name">print</span>(y)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>结果如下：</p>
                <figure class="highlight angelscript">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">Tensor(<span class="string">"conv2d/BiasAdd:0"</span>, shape=(?, <span class="number">19</span>, <span class="number">19</span>, <span class="number">6</span>), dtype=<span class="built_in">float</span>32)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>结果就变成了 [?, 19, 19, 6]，这是因为步长默认为 1，卷积核大小为 2 x 2，所以得到的结果的高宽即为 (20 - (2 - 1)) x (20 - (2 - 1)) = 19 x 19。 当然卷积核我们也可以变换大小，传入一个列表形式：</p>
                <figure class="highlight angelscript">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">x = tf.layers.Input(shape=[<span class="number">20</span>, <span class="number">20</span>, <span class="number">3</span>])</span><br><span class="line">y = tf.layers.conv2d(x, filters=<span class="number">6</span>, kernel_size=[<span class="number">2</span>, <span class="number">3</span>])</span><br><span class="line">print(y)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>这时我们的卷积核大小变成了 2 x 3，即高为 2，宽为 3，结果就变成了 [?, 19, 18, 6]，这是因为步长默认为 1，卷积核大小为 2 x 2，所以得到的结果的高宽即为 (20 - (2 - 1)) x (20 - (3 - 1)) = 19 x 18。 如果我们将步长也设置一下，也传入列表形式：</p>
                <figure class="highlight angelscript">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">x = tf.layers.Input(shape=[<span class="number">20</span>, <span class="number">20</span>, <span class="number">3</span>])</span><br><span class="line">y = tf.layers.conv2d(x, filters=<span class="number">6</span>, kernel_size=[<span class="number">2</span>, <span class="number">3</span>], strides=[<span class="number">2</span>, <span class="number">2</span>])</span><br><span class="line">print(y)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>这时卷积核大小变成了 2 x 3，步长变成了 2 x 2，所以结果的高宽为 ceil(20 - (2- 1)) / 2 x ceil(20 - (3- 1)) / 2 = 10 x 9，得到的结果即为 [?, 10, 9, 6]。 运行结果如下：</p>
                <figure class="highlight angelscript">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">Tensor(<span class="string">"conv2d_4/BiasAdd:0"</span>, shape=(?, <span class="number">10</span>, <span class="number">9</span>, <span class="number">6</span>), dtype=<span class="built_in">float</span>32)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>另外我们还可以传入激活函数，或者禁用 bias 等操作，实例如下：</p>
                <figure class="highlight routeros">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">x = tf.layers.Input(shape=[20, 20, 3])</span><br><span class="line">y = tf.layers.conv2d(x, <span class="attribute">filters</span>=6, <span class="attribute">kernel_size</span>=2, <span class="attribute">activation</span>=tf.nn.relu, <span class="attribute">use_bias</span>=<span class="literal">False</span>)</span><br><span class="line"><span class="builtin-name">print</span>(y)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>这样我们就将激活函数改成了 relu，同时禁用了 bias，运行结果如下：</p>
                <figure class="highlight angelscript">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">Tensor(<span class="string">"conv2d_5/Relu:0"</span>, shape=(?, <span class="number">19</span>, <span class="number">19</span>, <span class="number">6</span>), dtype=<span class="built_in">float</span>32)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>另外还有反卷积操作，反卷积顾名思义即卷积的反向操作，即输入卷积的结果，得到卷积前的结果，其参数用法是完全一样的，例如：</p>
                <figure class="highlight routeros">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">x = tf.layers.Input(shape=[20, 20, 3])</span><br><span class="line">y = tf.layers.conv2d_transpose(x, <span class="attribute">filters</span>=6, <span class="attribute">kernel_size</span>=2, <span class="attribute">strides</span>=2)</span><br><span class="line"><span class="builtin-name">print</span>(y)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>例如此处输入的图像高宽为 20 x 20，经过卷积核为 2，步长为 2 的反卷积处理，得到的结果高宽就变为了 40 x 40，结果如下：</p>
                <figure class="highlight angelscript">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">Tensor(<span class="string">"conv2d_transpose/BiasAdd:0"</span>, shape=(?, <span class="number">40</span>, <span class="number">40</span>, <span class="number">6</span>), dtype=<span class="built_in">float</span>32)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <h3 id="pooling"><a href="#pooling" class="headerlink" title="pooling"></a>pooling</h3>
                <p>pooling，即池化，layers 模块提供了多个池化方法，这几个池化方法都是类似的，包括 max_pooling1d()、max_pooling2d()、max_pooling3d()、average_pooling1d()、average_pooling2d()、average_pooling3d()，分别代表一维二维三维最大和平均池化方法，它们都定义在 tensorflow/python/layers/pooling.py 中，这里以 max_pooling2d() 方法为例进行介绍。</p>
                <figure class="highlight routeros">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">max_pooling2d(</span><br><span class="line">    inputs,</span><br><span class="line">    pool_size,</span><br><span class="line">    strides,</span><br><span class="line">    <span class="attribute">padding</span>=<span class="string">'valid'</span>,</span><br><span class="line">    <span class="attribute">data_format</span>=<span class="string">'channels_last'</span>,</span><br><span class="line">    <span class="attribute">name</span>=None</span><br><span class="line">)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>参数说明如下：</p>
                <ul>
                  <li>inputs: 必需，即需要池化的输入对象，必须是 4 维的。</li>
                  <li>pool_size：必需，池化窗口大小，必须是一个数字（高和宽都是此数字）或者长度为 2 的列表（分别代表高、宽）。</li>
                  <li>strides：必需，池化步长，必须是一个数字（高和宽都是此数字）或者长度为 2 的列表（分别代表高、宽）。</li>
                  <li>padding：可选，默认 valid，padding 的方法，valid 或者 same，大小写不区分。</li>
                  <li>data_format：可选，默认 channels_last，分为 channels_last 和 channels_first 两种模式，代表了输入数据的维度类型，如果是 channels_last，那么输入数据的 shape 为 (batch, height, width, channels)，如果是 channels_first，那么输入数据的 shape 为 (batch, channels, height, width)。</li>
                  <li>name：可选，默认 None，池化层的名称。</li>
                </ul>
                <p>返回值： 经过池化处理后的 Tensor。 下面我们用一个实例来感受一下：</p>
                <figure class="highlight routeros">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">x = tf.layers.Input(shape=[20, 20, 3])</span><br><span class="line"><span class="builtin-name">print</span>(x)</span><br><span class="line">y = tf.layers.conv2d(x, <span class="attribute">filters</span>=6, <span class="attribute">kernel_size</span>=3, <span class="attribute">padding</span>=<span class="string">'same'</span>)</span><br><span class="line"><span class="builtin-name">print</span>(y)</span><br><span class="line">p = tf.layers.max_pooling2d(y, <span class="attribute">pool_size</span>=2, <span class="attribute">strides</span>=2)</span><br><span class="line"><span class="builtin-name">print</span>(p)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>在这里我们首先指定了输入 x，shape 为 [20, 20, 3]，然后对其进行了卷积计算，然后池化，最后得到池化后的结果。结果如下：</p>
                <figure class="highlight angelscript">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">Tensor(<span class="string">"input_layer_1:0"</span>, shape=(?, <span class="number">20</span>, <span class="number">20</span>, <span class="number">3</span>), dtype=<span class="built_in">float</span>32)</span><br><span class="line">Tensor(<span class="string">"conv2d/BiasAdd:0"</span>, shape=(?, <span class="number">20</span>, <span class="number">20</span>, <span class="number">6</span>), dtype=<span class="built_in">float</span>32)</span><br><span class="line">Tensor(<span class="string">"max_pooling2d/MaxPool:0"</span>, shape=(?, <span class="number">10</span>, <span class="number">10</span>, <span class="number">6</span>), dtype=<span class="built_in">float</span>32)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>可以看到这里池化窗口用的是 2，步长也是 2，所以原本卷积后 shape 为 [?, 20, 20, 6] 的结果就变成了 [?, 10, 10, 6]。</p>
                <h3 id="dropout"><a href="#dropout" class="headerlink" title="dropout"></a>dropout</h3>
                <p>dropout 是指在深度学习网络的训练过程中，对于神经网络单元，按照一定的概率将其暂时从网络中丢弃，可以用来防止过拟合，layers 模块中提供了 dropout() 方法来实现这一操作，定义在 tensorflow/python/layers/core.py。下面我们来说明一下它的用法。</p>
                <figure class="highlight routeros">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">dropout(</span><br><span class="line">    inputs,</span><br><span class="line">    <span class="attribute">rate</span>=0.5,</span><br><span class="line">    <span class="attribute">noise_shape</span>=None,</span><br><span class="line">    <span class="attribute">seed</span>=None,</span><br><span class="line">    <span class="attribute">training</span>=<span class="literal">False</span>,</span><br><span class="line">    <span class="attribute">name</span>=None</span><br><span class="line">)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>参数说明如下：</p>
                <ul>
                  <li>inputs：必须，即输入数据。</li>
                  <li>rate：可选，默认为 0.5，即 dropout rate，如设置为 0.1，则意味着会丢弃 10% 的神经元。</li>
                  <li>noise_shape：可选，默认为 None，int32 类型的一维 Tensor，它代表了 dropout mask 的 shape，dropout mask 会与 inputs 相乘对 inputs 做转换，例如 inputs 的 shape 为 (batch_size, timesteps, features)，但我们想要 droput mask 在所有 timesteps 都是相同的，我们可以设置 noise_shape=[batch_size, 1, features]。</li>
                  <li>seed：可选，默认为 None，即产生随机熟的种子值。</li>
                  <li>training：可选，默认为 False，布尔类型，即代表了是否标志位 training 模式。</li>
                  <li>name：可选，默认为 None，dropout 层的名称。</li>
                </ul>
                <p>返回： 经过 dropout 层之后的 Tensor。 我们用一个实例来感受一下：</p>
                <figure class="highlight stylus">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">x = tf<span class="selector-class">.layers</span>.Input(shape=[<span class="number">32</span>])</span><br><span class="line"><span class="function"><span class="title">print</span><span class="params">(x)</span></span></span><br><span class="line">y = tf<span class="selector-class">.layers</span>.dense(x, <span class="number">16</span>, activation=tf<span class="selector-class">.nn</span>.softmax)</span><br><span class="line"><span class="function"><span class="title">print</span><span class="params">(y)</span></span></span><br><span class="line">d = tf<span class="selector-class">.layers</span>.dropout(y, rate=<span class="number">0.2</span>)</span><br><span class="line"><span class="function"><span class="title">print</span><span class="params">(d)</span></span></span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>运行结果：</p>
                <figure class="highlight stylus">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line"><span class="function"><span class="title">Tensor</span><span class="params">(<span class="string">"input_layer_1:0"</span>, shape=(?, <span class="number">32</span>)</span></span>, dtype=float32)</span><br><span class="line"><span class="function"><span class="title">Tensor</span><span class="params">(<span class="string">"dense/Softmax:0"</span>, shape=(?, <span class="number">16</span>)</span></span>, dtype=float32)</span><br><span class="line"><span class="function"><span class="title">Tensor</span><span class="params">(<span class="string">"dropout/Identity:0"</span>, shape=(?, <span class="number">16</span>)</span></span>, dtype=float32)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>在这里我们使用 dropout() 方法实现了 droput 操作，并制定 dropout rate 为 0.2，最后输出结果的 shape 和原来是一致的。</p>
                <h3 id="flatten"><a href="#flatten" class="headerlink" title="flatten"></a>flatten</h3>
                <p>flatten() 方法可以对 Tensor 进行展平操作，定义在 tensorflow/python/layers/core.py。</p>
                <figure class="highlight fortran">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">flatten(</span><br><span class="line">    inputs,</span><br><span class="line">    <span class="keyword">name</span>=<span class="keyword">None</span></span><br><span class="line">)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>参数说明如下：</p>
                <ul>
                  <li>inputs：必需，即输入数据。</li>
                  <li>name：可选，默认为 None，即该层的名称。</li>
                </ul>
                <p>返回结果： 展平后的 Tensor。 下面我们用一个实例来感受一下：</p>
                <figure class="highlight stylus">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">x = tf<span class="selector-class">.layers</span>.Input(shape=[<span class="number">5</span>, <span class="number">6</span>])</span><br><span class="line"><span class="function"><span class="title">print</span><span class="params">(x)</span></span></span><br><span class="line">y = tf<span class="selector-class">.layers</span>.flatten(x)</span><br><span class="line"><span class="function"><span class="title">print</span><span class="params">(y)</span></span></span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>运行结果：</p>
                <figure class="highlight stylus">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line"><span class="function"><span class="title">Tensor</span><span class="params">(<span class="string">"input_layer_1:0"</span>, shape=(?, <span class="number">5</span>, <span class="number">6</span>)</span></span>, dtype=float32)</span><br><span class="line"><span class="function"><span class="title">Tensor</span><span class="params">(<span class="string">"flatten/Reshape:0"</span>, shape=(?, <span class="number">30</span>)</span></span>, dtype=float32)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>这里输入数据的 shape 为 [?, 5, 6]，经过 flatten 层之后，就会变成 [?, 30]，即将除了第一维的数据维度相乘，对原 Tensor 进行展平。 假如第一维是一个已知的数的话，它依然还是同样的处理，示例如下：</p>
                <figure class="highlight stylus">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">x = tf.placeholder(shape=[<span class="number">5</span>, <span class="number">6</span>, <span class="number">2</span>], dtype=tf.float32)</span><br><span class="line"><span class="function"><span class="title">print</span><span class="params">(x)</span></span></span><br><span class="line">y = tf<span class="selector-class">.layers</span>.flatten(x)</span><br><span class="line"><span class="function"><span class="title">print</span><span class="params">(y)</span></span></span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>结果如下：</p>
                <figure class="highlight stylus">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line"><span class="function"><span class="title">Tensor</span><span class="params">(<span class="string">"Placeholder:0"</span>, shape=(<span class="number">5</span>, <span class="number">6</span>, <span class="number">2</span>)</span></span>, dtype=float32)</span><br><span class="line"><span class="function"><span class="title">Tensor</span><span class="params">(<span class="string">"flatten_2/Reshape:0"</span>, shape=(<span class="number">5</span>, <span class="number">12</span>)</span></span>, dtype=float32)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <h2 id="类"><a href="#类" class="headerlink" title="类"></a>类</h2>
                <p>除了如上的方法，其实我们还可以直接使用类来进行操作，实际上看方法的实现就是实例化了其对应的类，下面我们首先说明一下有哪些类可以使用：</p>
                <ul>
                  <li>class AveragePooling1D: 一维平均池化层类</li>
                  <li>class AveragePooling2D: 二维平均池化层类</li>
                  <li>class AveragePooling3D: 三维平均池化层类</li>
                  <li>class BatchNormalization: 批量标准化层类</li>
                  <li>class Conv1D: 一维卷积层类</li>
                  <li>class Conv2D: 二维卷积层类</li>
                  <li>class Conv2DTranspose: 二维反卷积层类</li>
                  <li>class Conv3D: 三维卷积层类</li>
                  <li>class Conv3DTranspose: 三维反卷积层类</li>
                  <li>class Dense: 全连接层类</li>
                  <li>class Dropout: Dropout 层类</li>
                  <li>class Flatten: Flatten 层类</li>
                  <li>class InputSpec: Input 层类</li>
                  <li>class Layer: 基类、父类</li>
                  <li>class MaxPooling1D: 一维最大池化层类</li>
                  <li>class MaxPooling2D: 二维最大池化层类</li>
                  <li>class MaxPooling3D: 三维最大池化层类</li>
                  <li>class SeparableConv2D: 二维深度可分离卷积层类</li>
                </ul>
                <p>其实类这些类都和上文介绍的方法是一一对应的，关于它的用法我们可以在方法的源码实现里面找到，下面我们以 Dense 类的用法为例来说明一下这些类的具体调用方法：</p>
                <figure class="highlight reasonml">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">x = tf.layers.<span class="constructor">Input(<span class="params">shape</span>=[32])</span></span><br><span class="line">dense = tf.layers.<span class="constructor">Dense(16, <span class="params">activation</span>=<span class="params">tf</span>.<span class="params">nn</span>.<span class="params">relu</span>)</span></span><br><span class="line">y = dense.apply(x)</span><br><span class="line">print(y)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>这里我们初始化了一个 Dense 类，它只接受一个必须参数，那就是 units，相比 dense() 方法来说它没有了 inputs，因此这个实例化的类和 inputs 是无关的，这样就相当于创建了一个 16 个神经元的全连接层。 但创建了不调用是没有用的，我们要将这个层构建到网络之中，需要调用它的 apply() 方法，而 apply() 方法就接收 inputs 这个参数，返回计算结果，运行结果如下：</p>
                <figure class="highlight stylus">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line"><span class="function"><span class="title">Tensor</span><span class="params">(<span class="string">"dense/Relu:0"</span>, shape=(?, <span class="number">16</span>)</span></span>, dtype=float32)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>因此我们可以发现，这些类在初始化的时候实际上是比其对应的方法少了 inputs 参数，其他的参数都是完全一致的，另外需要调用 apply() 方法才可以应用该层并将其构建到模型中。 所以其他的类的用法在此就不一一赘述了，初始化的参数可以类比其对应的方法，实例化类之后，调用 apply() 方法，可以达到同样的构建模型的效果。</p>
                <h2 id="结语"><a href="#结语" class="headerlink" title="结语"></a>结语</h2>
                <p>以上便是 TensorFlow layers 模块的详细用法说明，更加详细的用法可以参考官方文档：<a href="https://www.tensorflow.org/api_docs/python/tf/layers" target="_blank" rel="noopener">https://www.tensorflow.org/api_docs/python/tf/layers</a>。 本节代码地址：<a href="https://github.com/AIDeepLearning/TensorFlowLayers" target="_blank" rel="noopener">https://github.com/AIDeepLearning/TensorFlowLayers</a>。</p>
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